Abstract
High levels of stress during pregnancy can have lasting effects on maternal and offspring health, which disproportionately impacts families facing financial strain, systemic racism, and other forms of social oppression. Developing ways to monitor daily life stress during pregnancy is important for reducing stress-related health disparities. We evaluated the feasibility and acceptability of using mobile health (mHealth) technology (i.e., wearable biosensors, smartphone-based ecological momentary assessment) to measure prenatal stress in daily life. Fifty pregnant women (67% receiving public assistance; 70% Black, 6% Multiracial, 24% White) completed 10 days of ambulatory assessment, in which they answered smartphone-based surveys six times a day and wore a chest-band device (movisens EcgMove4) to monitor their heart rate, heart rate variability, and activity level. Feasibility and acceptability were evaluated using behavioral meta-data and participant feedback. Findings supported the feasibility and acceptability of mHealth methods: Participants answered approximately 75% of the surveys per day and wore the device for approximately 10 hours per day. Perceived burden was low. Notably, participants with higher reported stressors and financial strain reported lower burden associated with the protocol than participants with fewer life stressors, highlighting the feasibility of mHealth technology for monitoring prenatal stress among pregnant populations living with higher levels of contextual stressors. Findings support the use of mHealth technology to measure prenatal stress in real-world, daily life settings, which shows promise for informing scalable, technology-assisted interventions that may help to reduce health disparities by enabling more accessible and comprehensive care during pregnancy.
Similar content being viewed by others
References
Alhusen, J. L., Bower, K. M., Epstein, E., & Sharps, P. (2016). Racial discrimination and adverse birth outcomes: An integrative review. Journal of Midwifery & Women’s Health, 61(6), 707–720. https://doi.org/10.1111/jmwh.12490.
Almeida, D. M. (2005). Resilience and vulnerability to daily stressors assessed via diary methods. Current Directions in Psychological Science, 14(2), 64–68. https://doi.org/10.1111/j.0963-7214.2005.00336.x.
Almeida, D. M., Wethington, E., & Kessler, R. C. (2002). The daily inventory of stressful events: An interview-based approach for measuring daily stressors. Assessment, 9(1), 41–55. https://doi.org/10.1177/1073191102091006.
Barnard, K. (1988). Difficult life circumstances scale. University of Washington.
Bernstein, M. J., Zawadzki, M. J., Juth, V., Benfield, J. A., & Smyth, J. M. (2018). Social interactions in daily life: Within-person associations between momentary social experiences and psychological and physical health indicators. Journal of Social and Personal Relationships, 35(3), 372–394. https://doi.org/10.1177/0265407517691366.
Bloom, T., Glass, N., Curry, A., Hernandez, M., R., & Houck, G. (2013). Maternal stress exposures, reactions, and priorities for stress reduction among low-income urban women. Journal of Midwifery & Women’s Health, 58(2), 167–174. https://doi.org/10.1111/j.1542-2011.2012.00197.x.
Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 385–396. https://doi.org/10.2307/2136404.
Collins, T. E., Akselrod, S., Altymysheva, A., Nga, P. T. Q., Banatvala, N., & Berlina, D. (2023). The promise of digital health technologies for integrated care for maternal and child health and non-communicable diseases. BMJ, 381. https://doi.org/10.1136/bmj-2022-071074.
da Silva, P. H. A., Aiquoc, K. M., Silva Nunes, A. D., da, Medeiros, W. R., de Souza, T. A., Jerez-Roig, J., & Barbosa, I. R. (2022). Prevalence of access to prenatal care in the first trimester of pregnancy among black women compared to other races/ethnicities: A systematic review and meta-analysis. Public Health Reviews, 43, 1604400. https://doi.org/10.3389/phrs.2022.1604400.
DiPietro, J. A., Christensen, A. L., & Costigan, K. A. (2008). The pregnancy experience scale–brief version. Journal of Psychosomatic Obstetrics & Gynecology, 29(4), 262–267. https://doi.org/10.1080/01674820802546220.
Dunkel Schetter, C. (2011). Psychological science on pregnancy: Stress processes, biopsychosocial models, and emerging research issues. Annual Review of Psychology, 62(1), 531–558. https://doi.org/10.1146/annurev.psych.031809.130727.
Entringer, S., Buss, C., Andersen, J., Chicz-DeMet, A., & Wadhwa, P. D. (2011). Ecological momentary assessment of maternal cortisol profiles over a multiple-day period predict the length of human gestation. Psychosomatic Medicine, 73(6), 469–474. https://doi.org/10.1097/PSY.0b013e31821fbf9a.
Entringer, S., Buss, C., & Wadhwa, P. D. (2015). Prenatal stress, development, health and disease risk: A psychobiological perspective—2015 Curt Richter Award Paper. Psychoneuroendocrinology, 62, 366–375. https://doi.org/10.1016/j.psyneuen.2015.08.019.
Gelaye, B., & Koenen, K. C. (2018). The intergenerational impact of prenatal stress: Time to focus on prevention? Biological Psychiatry, 83(2), 92–93. https://doi.org/10.1016/j.biopsych.2017.11.004.
Giurgescu, C., Engeland, C. G., Templin, T. N., Zenk, S. N., Koenig, M. D., & Garfield, L. (2016). Racial discrimination predicts greater systemic inflammation in pregnant African American women. Applied Nursing Research, 32, 98–103. https://doi.org/10.1016/j.apnr.2016.06.008.
Glover, V. (2011). Annual Research Review: Prenatal stress and the origins of psychopathology: An evolutionary perspective. Journal of Child Psychology and Psychiatry, 52(4), 356–367. https://doi.org/10.1111/j.1469-7610.2011.02371.x.
Glover, V. (2015). Prenatal stress and its effects on the fetus and the child: Possible underlying biological mechanisms. Perinatal Programming of Neurodevelopment (pp. 269–283). Springer. https://doi.org/10.1007/978-1-4939-1372-5_13.
Gyselaers, W., Lanssens, D., Perry, H., & Khalil, A. (2019). Mobile health applications for prenatal assessment and monitoring. Current Pharmaceutical Design, 25(5), 615–623.
Kanner, A. D., Coyne, J. C., Schaefer, C., & Lazarus, R. S. (1981). Comparison of two modes of stress measurement: Daily hassles and uplifts versus major life events. Journal of Behavioral Medicine, 4, 1–39. https://doi.org/10.1007/bf00844845.
Kim, D. R., Bale, T. L., & Epperson, C. N. (2015). Prenatal programming of mental illness: Current understanding of relationship and mechanisms. Current Psychiatry Reports, 17(2), 5. https://doi.org/10.1007/s11920-014-0546-9.
Latendresse, G. (2009). The interaction between chronic stress and pregnancy: Preterm birth from a biobehavioral perspective. Journal of Midwifery & Women’s Health, 54(1), 8–17. https://doi.org/10.1016/j.jmwh.2008.08.001.
Lecomte, T., Potvin, S., Corbière, M., Guay, S., Samson, C., Cloutier, B., Francoeur, A., Pennou, A., & Khazaal, Y. (2020). Mobile apps for mental health issues: Meta-review of meta-analyses. JMIR mHealth and uHealth, 8(5), e17458. https://doi.org/10.2196/17458.
Lefmann, T., & Combs-Orme, T. (2014). Prenatal stress, poverty, and child outcomes. Child and Adolescent Social Work Journal, 31(6), 577–590. https://doi.org/10.1007/s10560-014-0340-x.
Li, J., Silvera-Tawil, D., Varnfield, M., Hussain, M. S., & Math, V. (2021). Users’ perceptions toward mHealth technologies for health and well-being monitoring in pregnancy care: Qualitative interview study. JMIR Formative Research, 5(12), e28628. https://doi.org/10.2196/28628.
Lu, M. C., & Halfon, N. (2003). Racial and ethnic disparities in birth outcomes: A life-course perspective. Maternal and Child Health Journal, 7(1), 13–30. https://doi.org/10.1023/A:1022537516969.
Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C., & Reger, G. M. (2011). mHealth for mental health: Integrating smartphone technology in behavioral healthcare. Professional Psychology: Research and Practice, 42(6), 505–512. https://doi.org/10.1037/a0024485.
Materia, F. T., Smyth, J. M., Heron, K. E., Hillemeier, M., Feinberg, M. E., Fonzi, P., & Downs, S., D (2018). Preconceptional health behavior change in women with overweight and obesity: Prototype for SMART strong healthy women intervention. mHealth, 4, 24. https://doi.org/10.21037/mhealth.2018.06.06.
Materia, F. T., & Smyth, J. M. (2021). Acceptability of intervention design factors in mHealth intervention research: Experimental factorial study. JMIR mHealth and uHealth, 9(7), e23303.
Mehra, R., Boyd, L. M., Magriples, U., Kershaw, T. S., Ickovics, J. R., & Keene, D. E. (2020). Black pregnant women get the most judgment: A qualitative study of the experiences of black women at the intersection of race, gender, and pregnancy. Women’s Health Issues, 30(6), 484–492. https://doi.org/10.1016/j.whi.2020.08.001.
Melia, R., Francis, K., Hickey, E., Bogue, J., Duggan, J., O’Sullivan, M., & Young, K. (2020). Mobile health technology interventions for suicide prevention: Systematic review. JMIR mHealth and uHealth, 8(1), e12516. https://doi.org/10.2196/12516.
Omowale, S. S., Casas, A., Lai, Y. H., Sanders, S. A., Hill, A. V., Wallace, M. L., & Mendez, D. D. (2021). Trends in stress throughout pregnancy and postpartum period during the COVID-19 pandemic: Longitudinal study using ecological momentary assessment and data from the Postpartum Mothers Mobile Study. JMIR Mental Health, 8(9), e30422. https://doi.org/10.2196/30422.
Pew Research Center (2021). Mobile Fact Sheet. Pew Research Center: Internet, Science & Tech. https://www.pewresearch.org/internet/fact-sheet/mobile/.
Robinson, A. M., Benzies, K. M., Cairns, S. L., Fung, T., & Tough, S. C. (2016). Who is distressed? A comparison of psychosocial stress in pregnancy across seven ethnicities. BMC Pregnancy and Childbirth, 16(1), 215. https://doi.org/10.1186/s12884-016-1015-8.
Rosenthal, L., Earnshaw, V. A., Lewis, T. T., Reid, A. E., Lewis, J. B., Stasko, E. C., Tobin, J. N., & Ickovics, J. R. (2015). Changes in experiences with discrimination across pregnancy and postpartum: Age differences and consequences for mental health. American Journal of Public Health, 105(4), 686–693. https://doi.org/10.2105/AJPH.2014.301906.
Runkle, J., Sugg, M., Boase, D., Galvin, S. L., & Coulson, C., C (2019). Use of wearable sensors for pregnancy health and environmental monitoring: Descriptive findings from the perspective of patients and providers. Digital Health, 5, 2055207619828220. https://doi.org/10.1177/2055207619828220.
Sakamoto, J. L., Carandang, R. R., Kharel, M., Shibanuma, A., Yarotskaya, E., Basargina, M., & Jimba, M. (2022). Effects of mHealth on the psychosocial health of pregnant women and mothers: A systematic review. British Medical Journal Open, 12(2), e056807. https://doi.org/10.1136/bmjopen-2021-056807.
Sandman, C. A., Davis, E. P., Buss, C., & Glynn, L. M. (2012). Exposure to prenatal psychobiological stress exerts programming influences on the mother and her fetus. Neuroendocrinology, 95(1), 7–21. https://doi.org/10.1159/000327017.
Sliwinski, M. J., Almeida, D. M., Smyth, J., & Stawski, R. S. (2009). Intraindividual change and variability in daily stress processes: Findings from two measurement-burst diary studies. Psychology and Aging, 24(4), 828–840. https://doi.org/10.1037/a0017925.
Smyth, J. M., Juth, V., Ma, J., & Sliwinski, M. (2017). A slice of life: Ecologically valid methods for research on social relationships and health across the life span. Social and Personality Psychology Compass, 11(10), e12356. https://doi.org/10.1111/spc3.12356.
Thayer, Z. M., & Kuzawa, C. W. (2015). Ethnic discrimination predicts poor self-rated health and cortisol in pregnancy: Insights from New Zealand. Social Science & Medicine (1982), 128, 36–42. https://doi.org/10.1016/j.socscimed.2015.01.003.
Tumuhimbise, W., Atukunda, E. C., Ayebaza, S., Katusiime, J., Mugyenyi, G., Pinkwart, N., & Musiimenta, A. (2020). Maternal health-related barriers and the potentials of mobile health technologies: Qualitative findings from a pilot randomized controlled trial in rural Southwestern Uganda. Journal of Family Medicine and Primary Care, 9(7), 3657–3662. https://doi.org/10.4103/jfmpc.jfmpc_281_20.
Tung, I., Hipwell, A. E., Grosse, P., Battaglia, L., Cannova, E., English, G., Quick, A. D., Llamas, B., Taylor, M., & Foust, J. E. (2023). Prenatal stress and externalizing behaviors in childhood and adolescence: A systematic review and meta-analysis. Psychological Bulletin. https://doi.org/10.1037/bul0000407. Advanced online publication.
Vilda, D., Wallace, M., Dyer, L., Harville, E., & Theall, K. (2019). Income inequality and racial disparities in pregnancy-related mortality in the US. SSM - Population Health, 9, 100477. https://doi.org/10.1016/j.ssmph.2019.100477.
Wakefield, C., Yao, L., Self, S., & Frasch, M. G. (2023). Wearable technology for health monitoring during pregnancy: An observational cross-sectional survey study. Archives of Gynecology and Obstetrics, 308(1), 73–78. https://doi.org/10.1007/s00404-022-06705-y.
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063. https://doi.org/10.1037/0022-3514.54.6.1063.
Wen, C. K. F., Junghaenel, D. U., Newman, D. B., Schneider, S., Mendez, M., Goldstein, S. E., Velasco, S., Smyth, J. M., & Stone, A. A. (2021). The effect of training on participant adherence with a reporting time frame for momentary subjective experiences in ecological momentary assessment: Cognitive interview study. JMIR Formative Research, 5(5), e28007. https://doi.org/10.2196/28007.
Zawadzki, M. J., Scott, S. B., Almeida, D. M., Lanza, S. T., Conroy, D. E., Sliwinski, M. J., Kim, J., Marcusson-Clavertz, D., Stawski, R. S., Green, P. M., Sciamanna, C. N., Johnson, J. A., & Smyth, J. M. (2019). Understanding stress reports in daily life: A coordinated analysis of factors associated with the frequency of reporting stress. Journal of Behavioral Medicine, 42(3), 545–560. https://doi.org/10.1007/s10865-018-00008-x.
Acknowledgements
Special thanks to the participants of the Pittsburgh Girls Study for their participation in this research and to our dedicated research team for their continued efforts.
Funding
This research was supported by the National Institute of Mental Health (K01MH123505) and the National Institute of Health Environmental influences on Child Health Outcomes (ECHO) Program (UH3OD023244).
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection, and analyses were performed by I.T. and U.B. The first draft of the manuscript was written by I.T.; all authors provided feedback on previous versions of the manuscript. All authors approved the final manuscript.
Corresponding author
Ethics declarations
Ethical approval
The authors have no conflicts of interests to disclose. The study was approved by the Institutional Review Boards (IRB) of California State University, Dominguez Hills and the University of Pittsburgh. Written informed consent was obtained from all participants prior to data collection.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Tung, I., Balaji, U., Hipwell, A.E. et al. Feasibility and acceptability of measuring prenatal stress in daily life using smartphone-based ecological momentary assessment and wearable physiological monitors. J Behav Med (2024). https://doi.org/10.1007/s10865-024-00484-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10865-024-00484-4